Senior Artificial Intelligence/Machine Learning Engineer
4 weeks ago
Description : Key Responsibilities (What You'll Do) :- You will be responsible for the full lifecycle of AI/ML services, from proof-of-concept to production deployment :- LLM & RAG System Development : Design, implement, and optimize LLM + Vector Database-based Retrieval-Augmented Generation (RAG) systems to power sophisticated, highly accurate, and domain-specific chat and knowledge retrieval applications.- Agentic AI Implementation : Architect and develop Agentic AI frameworks and systems tailored for complex FinTech use cases (e.g., automated decision-making, intelligent workflow management).- Model Training & Fine-Tuning : Conduct research and execute the training and fine-tuning of state-of-the-art transformer-based NLP models (LLMs) for critical classification, summarization, and generation tasks.- Information Extraction Pipelines : Build and optimize high-throughput, accurate document classification and information extraction pipelines, ensuring data quality and system reliability.- Computer Vision Integration : Apply Computer Vision (CV) and OCR techniques to tackle document- and image-based challenges, enhancing our ability to process complex financial documentation.- Production Deployment : Deploy, monitor, and maintain scalable, low-latency ML services using modern MLOps principles and cloud environments (e.g., AWS, GCP, Azure).- Code Quality : Uphold the highest standards for code quality, system performance, and semi-OCR methodology, ensuring robust and maintainable infrastructure.Required Qualifications (What Were Looking For) :Core Experience & Technical Skills : - Experience : 3+ years of hands-on experience as an ML Engineer, Data Scientist, or NLP Engineer, with a focus on building and deploying production-level systems.- Programming : Expert-level proficiency in Python and its ecosystem, with a focus on performance-critical code.- ML Frameworks : Strong command of major Machine Learning frameworks, including PyTorch (preferred), TensorFlow, and the Hugging Face ecosystem for transformer models.- Vector Databases : Proven experience implementing solutions using any modern vector database (e.g., Qdrant, Pinecone, Chroma, Milvus).- NLP/LLM Specialization : Demonstrated success in working with Large Language Models (LLMs), prompt engineering, and the principles of Agentic AI. (ref:hirist.tech)
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